Reconstruction of the Past Climate in Southern Portugal from Geothermal Data

The study of the past climate in the territory of mainland Portugal using geothermal data started in 1996. From an initial set of about 90 temperature logs obtained Portugal, eight were chosen as good for estimating ground surface temperature (GST) in the past. The main results from the analysis of the collected geothermal data show, on average, that there has been an increase of the atmosphere mean surface temperature of about 1 K since the middle of the nineteenth century. This conclusion agrees with the results obtained from the analysis of air temperature records that were obtained in the Lisbon weather station since 1856. With the objective of improving the reconstruction of GST history in Portugal one of the eight wells (the TGQC-1 well) was cased and is being used for repeated temperature logging since 1997. The results of the temperature measurements repetition indicate that the warming trend continues to the present day, accelerating in the last 10 to 15 years. Since 2005 a geothermal climate change observatory was installed in the TGQC-1 well to study the air-ground coupling

Geophysical study in a Neolithic burial mound in Proença-a-Nova, Portugal

The municipality of Proença-a-Nova in cooperation with Associação de Estudos do Alto Tejo, and the University of Evora have been excavating and, at the same time, doing geophysical surveys in a neolithic burial mound known as Cabeço da Anta near the town of Proença-a-Nova in Portugal. Excavation in the site started in the summer of 2013 and still goes on; however, before excavating a geophysical survey with ground penetrating radar (GPR) and electrical resistivity tomography (ERT) methods were used as an attempt to find the location of the mound’s chamber and its main entrance. From the two geophysical methods used up to now only the electrical resistivity tomography has shown a good potential to detect and delineate the dolmen’s structure buried in the mound. On the contrary, ground penetrating radar was not very useful to detect those same structures because of the high diffraction caused by the surface rocks. With the ERT profiles it was possible to infer that the mound was covered by blocks of rocks which were placed on top of clay and silt. The slabs of schist that compose the walls and the cover of the dolmen chamber were also identified by the ERT profiles. The geophysical surveys done up to now were not able to find the dolmen’s main entrance. The comparison of the interpretations done using electrical resistivity tomography with the results of the excavation shows a good coincidence which, to a certain extent, validates the use of the ERT method in Cabeço da Anta

Flexible Time Series Matching for Clinical and Behavioral Data

Time Series data became broadly applied by the research community in the last decades after a massive explosion of its availability. Nonetheless, this rise required an improvement in the existing analysis techniques which, in the medical domain, would help specialists to evaluate their patients condition. One of the key tasks in time series analysis is pattern recognition (segmentation and classification). Traditional methods typically perform subsequence matching, making use of a pattern template and a similarity metric to search for similar sequences throughout time series. However, real-world data is noisy and variable (morphological distortions), making a template-based exact matching an elementary approach. Intending to increase flexibility and generalize the pattern searching tasks across domains, this dissertation proposes two Deep Learning-based frameworks to solve pattern segmentation and anomaly detection problems. Regarding pattern segmentation, a Convolution/Deconvolution Neural Network is proposed, learning to distinguish, point-by-point, desired sub-patterns from background content within a time series. The proposed framework was validated in two use-cases: electrocardiogram (ECG) and inertial sensor-based human activity (IMU) signals. It outperformed two conventional matching techniques, being capable of notably detecting the targeted cycles even in noise-corrupted or extremely distorted signals, without using any reference template nor hand-coded similarity scores. Concerning anomaly detection, the proposed unsupervised framework uses the reconstruction ability of Variational Autoencoders and a local similarity score to identify non-labeled abnormalities. The proposal was validated in two public ECG datasets (MITBIH Arrhythmia and ECG5000), performing cardiac arrhythmia identification. Results indicated competitiveness relative to recent techniques, achieving detection AUC scores of 98.84% (ECG5000) and 93.32% (MIT-BIH Arrhythmia).Dados de séries temporais tornaram-se largamente aplicados pela comunidade científica nas últimas decadas após um aumento massivo da sua disponibilidade. Contudo, este aumento exigiu uma melhoria das atuais técnicas de análise que, no domínio clínico, auxiliaria os especialistas na avaliação da condição dos seus pacientes. Um dos principais tipos de análise em séries temporais é o reconhecimento de padrões (segmentação e classificação). Métodos tradicionais assentam, tipicamente, em técnicas de correspondência em subsequências, fazendo uso de um padrão de referência e uma métrica de similaridade para procurar por subsequências similares ao longo de séries temporais. Todavia, dados do mundo real são ruidosos e variáveis (morfologicamente), tornando uma correspondência exata baseada num padrão de referência uma abordagem rudimentar. Pretendendo aumentar a flexibilidade da análise de séries temporais e generalizar tarefas de procura de padrões entre domínios, esta dissertação propõe duas abordagens baseadas em Deep Learning para solucionar problemas de segmentação de padrões e deteção de anomalias. Acerca da segmentação de padrões, a rede neuronal de Convolução/Deconvolução proposta aprende a distinguir, ponto a ponto, sub-padrões pretendidos de conteúdo de fundo numa série temporal. O modelo proposto foi validado em dois casos de uso: sinais eletrocardiográficos (ECG) e de sensores inerciais em atividade humana (IMU). Este superou duas técnicas convencionais, sendo capaz de detetar os ciclos-alvo notavelmente, mesmo em sinais corrompidos por ruído ou extremamente distorcidos, sem o uso de nenhum padrão de referência nem métricas de similaridade codificadas manualmente. A respeito da deteção de anomalias, a técnica não supervisionada proposta usa a capacidade de reconstrução dos Variational Autoencoders e uma métrica de similaridade local para identificar anomalias desconhecidas. A proposta foi validada na identificação de arritmias cardíacas em duas bases de dados públicas de ECG (MIT-BIH Arrhythmia e ECG5000). Os resultados revelam competitividade face a técnicas recentes, alcançando métricas AUC de deteção de 93.32% (MIT-BIH Arrhythmia) e 98.84% (ECG5000)

The pandemonics of informal credit markets

Credit markets are at the core of any economic crisis, and informal loans are largely understudied. We collect a dataset on an online informal lending community to study the impact that the 2020 pandemic crisis had on informal credit markets. We find that these informal loans are short duration, expensive and that borrowers and lenders exhibit some sense of community. Our results suggest that the financial hardship imposed by stay at home orders is perceived as persistent, and borrowers expect lower future income, hence reducing loan demand. Moreover, loans directly associated with the pandemic are more likely to be transacted by newcomers to this market, and mentioning the pandemic in a loan request lowers the chance that it originates a loan. The absence of an increase of violations of community rules and the reduction in promised repayment time highlights the importance of informal credit communities in hard times.info:eu-repo/semantics/publishedVersio

Intelligent simulation of coastal ecosystems

Tese de doutoramento. Engenharia Informática. Faculdade de Engenharia. Universidade do Porto, Faculdade de Ciência e Tecnologia. Universidade Fernando Pessoa. 201

A model to evaluate vertical handovers on JRRM

In a heterogeneous cellular networks environment, users behaviour and network deployment configuration parameters have an impact on the overall Quality of Service. This paper proposes a new and simple model that, on the one hand, explores the users behaviour impact on the network by having mobility, multi-service usage and traffic generation profiles as inputs, and on the other, enables the network setup configuration evaluation impact on the Joint Radio Resource Management (JRRM), assessing some basic JRRM performance indicators, like Vertical Handover (VHO) probabilities, average bit rates, and number of active users, among others. VHO plays an important role in fulfilling seamless users sessions transfer when mobile terminals cross different Radio Access Technologies (RATs) boundaries. Results show that high bit rate RATs suffer and generate more influence from/on other RATs, by producing additional signalling traffic to a JRRM entity. Results also show that the VHOs probability can range from 5 up to 65%, depending on RATs cluster radius and users mobility profile